Virtual trial assembly of large steel members with bolted connections based on point cloud data

Cheng, Guozhong; Liu, Jiepeng; Cui, Na; Xu, Cheng‐Ran; Tang, Jin Peng

doi:10.1016/j.autcon.2023.104866

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…According to the density distribution of the projected point cloud of the bolt hole surface, the hole center coordinates can be obtained more accurately, which increases the robustness of the bolt hole extraction algorithm. The general equation of the 3D space plane is shown in Equation (1).…”

Section: Planar Point Cloud Cluster Projectionmentioning

confidence: 99%

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Extraction of Feature Information from Point Cloud of Large Volume Steel Truss Members

Liang,

Zhang,

Guo

et al. 2024

Advances in Civil Engineering

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In order to address the issues pertaining to the subjective nature and limited precision associated with selecting feature points in the point cloud of a large steel truss structure, this study proposes a batch automatic extraction approach for identifying key feature information, including boundaries, corner points, and bolt holes of large steel truss components. This method relies on the nested application of established processing algorithms such as Euclidean clusters, regional growth clusters, and random sampling consensus. In addition, a novel approach is suggested for validating the precision of feature information extraction through the utilization of standard theoretical models. The results of the experimental and large-scale lower chord tests demonstrate that our approach is not dependent on specialized software, exhibits excellent efficiency, and possesses an acceptable degree of automation. The findings of this study can provide accurate data support for reverse modeling, virtual trial assembly, and dimensional inspection of steel truss components.

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Section: Planar Point Cloud Cluster Projectionmentioning

confidence: 99%

“…The utilization of computer technology and 3D measurement technology has supplanted conventional approaches, such as outbound quality checking, overall deformation monitoring, and preassembly of steel structures. The execution of these duties has transitioned to a digital format facilitated by point clouds [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Feature Information from Point Cloud of Large Volume Steel Truss Members

Liang,

Zhang,

Guo

et al. 2024

Advances in Civil Engineering

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“…However, to align multiple scans of a bridge to a unified coordinate system, the utilization of multi-view registration techniques is essential. In engineering practice, artificial markers such as target spheres [ 10 ] and target papers [ 11 ] have been commonly used to assist registration and improve registration accuracy when dealing with multiple scans. Although the registration results using these artificial markers are highly reliable, placing artificial markers is time-consuming and costly [ 12 ], especially for large infrastructure like bridges.…”

Section: Introductionmentioning

confidence: 99%

Template-Guided Hierarchical Multi-View Registration Framework of Unordered Bridge Terrestrial Laser Scanning Data

Xiong,

Cui,

Liu

et al. 2024

Sensors

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The registration of bridge point cloud data (PCD) is an important preprocessing step for tasks such as bridge modeling, deformation detection, and bridge health monitoring. However, most existing research on bridge PCD registration only focused on pairwise registration, and payed insufficient attention to multi-view registration. In addition, to recover the overlaps of unordered multiple scans and obtain the merging order, extensive pairwise matching and the creation of a fully connected graph of all scans are often required, resulting in low efficiency. To address these issues, this paper proposes a marker-free template-guided method to align multiple unordered bridge PCD to a global coordinate system. Firstly, by aligning each scan to a given registration template, the overlaps between all the scans are recovered. Secondly, a fully connected graph is created based on the overlaps and scanning locations, and then a graph-partition algorithm is utilized to construct the scan-blocks. Then, the coarse-to-fine registration is performed within each scan-block, and the transformation matrix of coarse registration is obtained using an intelligent optimization algorithm. Finally, global block-to-block registration is performed to align all scans to a unified coordinate reference system. We tested our framework on different bridge point cloud datasets, including a suspension bridge and a continuous rigid frame bridge, to evaluate its accuracy. Experimental results demonstrate that our method has high accuracy.

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“…However, this process is intricate, resulting in low extraction accuracy and underutilization of the high-precision 3D coordinates of the point clouds. Currently, the accuracy of target extraction algorithms relies on high-density point clouds, but this increases the monitoring time [13]. Tus, using low-density point cloud data to acquire high-precision target centers is the key to fast data extraction.…”

Section: Introductionmentioning

confidence: 99%

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Liang,

Zhang,

Zhang

et al. 2024

Structural Control and Health Monitoring

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Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking.

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Virtual trial assembly of large steel members with bolted connections based on point cloud data

Cited by 6 publications

References 31 publications

Extraction of Feature Information from Point Cloud of Large Volume Steel Truss Members

Extraction of Feature Information from Point Cloud of Large Volume Steel Truss Members

Template-Guided Hierarchical Multi-View Registration Framework of Unordered Bridge Terrestrial Laser Scanning Data

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

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